
"""simple docstring"""



import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
a       :						Union[str, Any]       		=		logging.get_logger(__name__)

a       :						Optional[int]       		=		{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
    '''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
    '''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
a       :						List[str]       		=		[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A       ,   _A   )     ->	List[Any]:





    """simple docstring"""

    for attribute in key.split("""."""   ):
        _UpperCAmelCase        = getattr(_A       ,   _A   )

    if weight_type is not None:
        _UpperCAmelCase        = getattr(_A       ,   _A   ).shape
    else:
        _UpperCAmelCase        = hf_pointer.shape

    assert hf_shape == value.shape, (
        F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
        F""" {value.shape} for {full_name}"""
    )

    if weight_type == "weight":
        _UpperCAmelCase        = value
    elif weight_type == "weight_g":
        _UpperCAmelCase        = value
    elif weight_type == "weight_v":
        _UpperCAmelCase        = value
    elif weight_type == "bias":
        _UpperCAmelCase        = value
    else:
        _UpperCAmelCase        = value

    logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""   )


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = []
    _UpperCAmelCase        = fairseq_model.state_dict()

    _UpperCAmelCase        = hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        _UpperCAmelCase        = False
        if "conv_layers" in name:
            load_conv_layer(
                _A       ,   _A       ,   _A       ,   _A       ,   hf_model.config.feat_extract_norm == """group"""       ,   )
            _UpperCAmelCase        = True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split("""w2v_model."""   )[-1] == name.split("""."""   )[0]:
                    _UpperCAmelCase        = True
                    if "*" in mapped_key:
                        _UpperCAmelCase        = name.split(_A   )[0].split("""."""   )[-2]
                        _UpperCAmelCase        = mapped_key.replace("""*"""       ,   _A   )
                    if "weight_g" in name:
                        _UpperCAmelCase        = """weight_g"""
                    elif "weight_v" in name:
                        _UpperCAmelCase        = """weight_v"""
                    elif "bias" in name and "relative_attention_bias" not in name:
                        _UpperCAmelCase        = """bias"""
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        _UpperCAmelCase        = """weight"""
                    else:
                        _UpperCAmelCase        = None

                    set_recursively(_A       ,   _A       ,   _A       ,   _A       ,   _A   )
                continue
        if not is_used:
            unused_weights.append(_A   )

    logger.warning(F"""Unused weights: {unused_weights}"""   )


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A       ,   _A   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase        = full_name.split("""conv_layers."""   )[-1]
    _UpperCAmelCase        = name.split("""."""   )
    _UpperCAmelCase        = int(items[0]   )
    _UpperCAmelCase        = int(items[1]   )

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
            )
            _UpperCAmelCase        = value
            logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
            )
            _UpperCAmelCase        = value
            logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                " found."
            )
            _UpperCAmelCase        = value
            logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
            )
            _UpperCAmelCase        = value
            logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
    else:
        unused_weights.append(_A   )


@torch.no_grad()
def 			_UpperCamelCase						(    _A       ,   _A       ,   _A=None   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = torch.load(_A   )
    _UpperCAmelCase        = WavLMConfigOrig(checkpoint["""cfg"""]   )
    _UpperCAmelCase        = WavLMOrig(_A   )
    model.load_state_dict(checkpoint["""model"""]   )
    model.eval()

    if config_path is not None:
        _UpperCAmelCase        = WavLMConfig.from_pretrained(_A   )
    else:
        _UpperCAmelCase        = WavLMConfig()

    _UpperCAmelCase        = WavLMModel(_A   )

    recursively_load_weights(_A       ,   _A   )

    hf_wavlm.save_pretrained(_A   )


if __name__ == "__main__":
 a       :						Dict       		=		argparse.ArgumentParser()
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
 a       :						str       		=		parser.parse_args()
 convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

"""simple docstring"""



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 		a_    (			_UpperCAmelCase  ,    _UpperCAmelCase  ,    unittest.TestCase						):
    a   :					Dict			     =					StableDiffusionSAGPipeline
    a   :					Any			     =					TEXT_TO_IMAGE_PARAMS
    a   :					Optional[Any]			     =					TEXT_TO_IMAGE_BATCH_PARAMS
    a   :					Optional[int]			     =					TEXT_TO_IMAGE_IMAGE_PARAMS
    a   :					Optional[int]			     =					TEXT_TO_IMAGE_IMAGE_PARAMS
    a   :					Tuple			     =					False







    def   _snake_case       (      self      :							List[Any]  )		->Tuple:






        '''simple docstring'''






        torch.manual_seed(0  )
        _UpperCAmelCase        = UNetaDConditionModel(
            block_out_channels=(32, 64)  ,       layers_per_block=2  ,       sample_size=32  ,       in_channels=4  ,       out_channels=4  ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,       cross_attention_dim=32  ,       )
        _UpperCAmelCase        = DDIMScheduler(
            beta_start=0.0_0_0_8_5  ,       beta_end=0.0_1_2  ,       beta_schedule="""scaled_linear"""  ,       clip_sample=__UpperCamelCase  ,       set_alpha_to_one=__UpperCamelCase  ,       )
        torch.manual_seed(0  )
        _UpperCAmelCase        = AutoencoderKL(
            block_out_channels=[32, 64]  ,       in_channels=3  ,       out_channels=3  ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]  ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]  ,       latent_channels=4  ,       )
        torch.manual_seed(0  )
        _UpperCAmelCase        = CLIPTextConfig(
            bos_token_id=0  ,       eos_token_id=2  ,       hidden_size=32  ,       intermediate_size=37  ,       layer_norm_eps=1e-05  ,       num_attention_heads=4  ,       num_hidden_layers=5  ,       pad_token_id=1  ,       vocab_size=10_00  ,       )
        _UpperCAmelCase        = CLIPTextModel(__UpperCamelCase  )
        _UpperCAmelCase        = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

        _UpperCAmelCase        = {
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components







    def   _snake_case       (      self      :							Union[str, Any]  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Any=0  )		->Any:






        '''simple docstring'''






        if str(__UpperCamelCase  ).startswith("""mps"""  ):
            _UpperCAmelCase        = torch.manual_seed(__UpperCamelCase  )
        else:
            _UpperCAmelCase        = torch.Generator(device=__UpperCamelCase  ).manual_seed(__UpperCamelCase  )
        _UpperCAmelCase        = {
            """prompt""": """.""",
            """generator""": generator,
            """num_inference_steps""": 2,
            """guidance_scale""": 1.0,
            """sag_scale""": 1.0,
            """output_type""": """numpy""",
        }
        return inputs







    def   _snake_case       (      self      :							Optional[Any]  )		->Optional[int]:






        '''simple docstring'''






        super().test_inference_batch_single_identical(expected_max_diff=3e-3  )







@slow
@require_torch_gpu
class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							Union[str, Any]  )		->str:






        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def   _snake_case       (      self      :							Tuple  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""  )
        _UpperCAmelCase        = sag_pipe.to(__UpperCamelCase  )
        sag_pipe.set_progress_bar_config(disable=__UpperCamelCase  )

        _UpperCAmelCase        = """."""
        _UpperCAmelCase        = torch.manual_seed(0  )
        _UpperCAmelCase        = sag_pipe(
            [prompt]  ,       generator=__UpperCamelCase  ,       guidance_scale=7.5  ,       sag_scale=1.0  ,       num_inference_steps=20  ,       output_type="""np"""  )

        _UpperCAmelCase        = output.images

        _UpperCAmelCase        = image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        _UpperCAmelCase        = np.array([0.1_5_6_8, 0.1_7_3_8, 0.1_6_9_5, 0.1_6_9_3, 0.1_5_0_7, 0.1_7_0_5, 0.1_5_4_7, 0.1_7_5_1, 0.1_9_4_9]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-2







    def   _snake_case       (      self      :							Union[str, Any]  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""  )
        _UpperCAmelCase        = sag_pipe.to(__UpperCamelCase  )
        sag_pipe.set_progress_bar_config(disable=__UpperCamelCase  )

        _UpperCAmelCase        = """."""
        _UpperCAmelCase        = torch.manual_seed(0  )
        _UpperCAmelCase        = sag_pipe(
            [prompt]  ,       generator=__UpperCamelCase  ,       guidance_scale=7.5  ,       sag_scale=1.0  ,       num_inference_steps=20  ,       output_type="""np"""  )

        _UpperCAmelCase        = output.images

        _UpperCAmelCase        = image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        _UpperCAmelCase        = np.array([0.3_4_5_9, 0.2_8_7_6, 0.2_5_3_7, 0.3_0_0_2, 0.2_6_7_1, 0.2_1_6_0, 0.3_0_2_6, 0.2_2_6_2, 0.2_3_7_1]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-2







    def   _snake_case       (      self      :							str  )		->str:






        '''simple docstring'''






        _UpperCAmelCase        = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""  )
        _UpperCAmelCase        = sag_pipe.to(__UpperCamelCase  )
        sag_pipe.set_progress_bar_config(disable=__UpperCamelCase  )

        _UpperCAmelCase        = """."""
        _UpperCAmelCase        = torch.manual_seed(0  )
        _UpperCAmelCase        = sag_pipe(
            [prompt]  ,       width=7_68  ,       height=5_12  ,       generator=__UpperCamelCase  ,       guidance_scale=7.5  ,       sag_scale=1.0  ,       num_inference_steps=20  ,       output_type="""np"""  ,       )

        _UpperCAmelCase        = output.images

        assert image.shape == (1, 5_12, 7_68, 3)